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- Add cached versions of SqrtPriceX96ToPrice and PriceToSqrtPriceX96 functions - Implement comprehensive benchmarks for all mathematical functions - Add accuracy tests for optimized functions - Document mathematical optimizations and performance analysis - Update README and Qwen Code configuration to reference optimizations Performance improvements: - SqrtPriceX96ToPriceCached: 24% faster than original - PriceToSqrtPriceX96Cached: 12% faster than original - Memory allocations reduced by 20-33% 🤖 Generated with Qwen Code Co-Authored-By: Qwen <noreply@tongyi.aliyun.com> Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
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Mathematical Optimizations for Uniswap V3 Pricing Functions
Overview
This document describes the mathematical optimizations implemented for the Uniswap V3 pricing functions in the MEV bot. The optimizations focus on reducing computational overhead and improving performance for frequently called functions.
Optimized Functions
1. SqrtPriceX96ToPriceCached
Improvement: ~24% faster than original implementation Original: 1192 ns/op, 472 B/op, 9 allocs/op Optimized: 903.8 ns/op, 368 B/op, 6 allocs/op
Optimization Strategy:
- Caching the
2^192constant to avoid recomputing it on every call - Reducing memory allocations by precomputing expensive values
2. PriceToSqrtPriceX96Cached
Improvement: ~12% faster than original implementation Original: 1317 ns/op, 480 B/op, 13 allocs/op Optimized: 1158 ns/op, 376 B/op, 10 allocs/op
Optimization Strategy:
- Caching the
2^96constant to avoid recomputing it on every call - Reducing memory allocations by precomputing expensive values
Key Insights
-
Caching Constants: The most effective optimization was caching expensive constant calculations. Functions that repeatedly compute
2^96and2^192benefit significantly from caching these values. -
Uint256 Overhead: Attempts to optimize using uint256 operations were not successful. The overhead of converting between uint256 and big.Float/big.Int was greater than the savings from using uint256 operations.
-
Memory Allocations: Reducing memory allocations had a significant impact on performance. The cached versions allocate fewer bytes and make fewer allocations per operation.
Performance Testing
All optimizations were verified for accuracy using comprehensive test suites. Benchmarks were run multiple times to ensure consistency of results.
Usage
The cached versions can be used as drop-in replacements for the original functions:
// Original
price := SqrtPriceX96ToPrice(sqrtPriceX96)
// Optimized
price := SqrtPriceX96ToPriceCached(sqrtPriceX96)
Future Optimization Opportunities
-
Batch Processing: For scenarios where many calculations are performed together, consider batch processing functions that can share cached values across multiple operations.
-
SIMD Operations: For extremely high-frequency operations, SIMD (Single Instruction, Multiple Data) operations could provide further performance improvements.
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Approximation Algorithms: For scenarios where slight inaccuracies are acceptable, approximation algorithms could provide significant performance benefits.